CN103177109A - Application ranking optimization method - Google Patents
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- CN103177109A CN103177109A CN2013101020953A CN201310102095A CN103177109A CN 103177109 A CN103177109 A CN 103177109A CN 2013101020953 A CN2013101020953 A CN 2013101020953A CN 201310102095 A CN201310102095 A CN 201310102095A CN 103177109 A CN103177109 A CN 103177109A
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Abstract
The invention relates to an application ranking optimization method in an application store. A technical problem to be solved is to provide the application ranking optimization method, and the method can carry on a comprehensive evaluation of multiple indexes for a software in order to enable the application ranking to be more reasonable. The technology scheme can be summed up as: the application ranking optimization method which includes the following steps: a) a system reads at least two kinds of parameters, one of the two kinds of the parameters is downloaded amount, and the rest of the parameters is the user review quantity and/or the application activation quantity and/or application uninstall quantity; b) parameters of various parameters are calculated respectively; c) the parameters of the various parameters are respectively and multiplied by and combined with the weighting quantity of the parameters to get the optimization ranking. The application ranking optimization method in the application store has the advantages of being simple and high-efficient, capable of objectively and fairly ranking for the application, capable of preventing ranking for a roughhewn software, and capable of maintaining the normal and ordered development of the application store. The application ranking optimization method is suitable for the application store.
Description
Technical field
The present invention relates to use in a kind of application shop the rank optimization method.
Background technology
Along with the development of technology, the "smart" products such as intelligent television, Intelligent set top box, smart mobile phone more and more are subject to consumer's favor.Accordingly, the software of using in the shop also increases like the mushrooms after rain rapidly.How the user of "smart" products is presented in the application of high-quality exactly by rank, allow user effort minimum cost and time choose the high-quality application that is fit to oneself, become to keep and use the emphasis that develops in a healthy way in the shop.Adopt at present the mode of single download to carry out rank to software, and its drawback progressively highlighting---third party software company often adopts the malicious downloading mode, promotes this application rank with this.Therefore, need improvement application rank mode, prevent that rough software from occupying list, so that ensure better the sound development of using the shop.
Summary of the invention
Technical matters to be solved by this invention is to provide a kind of application rank optimization method, and the method can be carried out to software the Comprehensive Assessment of many index, so that it is more reasonable to use rank.
The technical solution adopted for the present invention to solve the technical problems is: use the rank optimization method, comprise the following steps:
The a system reads at least two kinds of parameters, and wherein a kind of is download, and all the other parameters are user comment amount and/or application activating amount and/or use discharging quantity;
B calculates respectively the parameter of each parameter;
C with the parameter of above-mentioned each parameter respectively with its weighted volumes rank that is optimized after merging that multiplies each other.
Concrete, the download in step a is for getting rid of repeated downloads amount value afterwards.
Preferably, in step b, the Parameters Calculation method of each parameter is respectively
Daily downloads * 3+ daily downloads * 2 of going forward before download parameter=today download * 8+ download * 5+ yesterday;
User comments amount parameter = (today the number of five-star reviews × 16 × 15% + today's four-star review several × 16 × 15% × 0.5 + today Samsung Comments - Today two star reviews several × 16 × 15% - today a Star Comments × 16 × 15% × 2) + (yesterday Map Comments × 16 × 15% × 80% + Prev four weeks Comments × 16 × 15% × 0.5 × 80% + Prev Samsung Comments × 80% - two weeks yesterday Comments × 16 × 15% × 80% - yesterday Star Comments × 16 × 15% × 2 × 80%) + (Prev Map Comments × 16 × 15% × 60% + Prev × 16 × four weeks Comments 15% × 0.5 × 60% + day before Samsung comments × 60% - the day before the two star reviews several × 16 × 15% × 60% - the day before a star reviews several × 16 × 15% × 2 × 60%) + (on the day before Five Star comments × 16 × 15% × 40% + on the day before the four-star review several × 16 × 15% × 0.5 × 40% + on the day before the Samsung comments × 40% - on the day before the two star reviews several × 16 × 15% × 40% - on the day before the one star reviews several × 16 × 15% × 2 × 40%);
The application activating amount parameter=today application activating quantity * 4+ application activating yesterday quantity * 2.5+ application activating day before yesterday quantity * 1.5+ upper day before yesterday of application activating quantity;
Use discharging quantity and use discharging quantity * 1.1 parameter=today.
Further, in step c, the weighted volumes of download accounts for 50%, and the summation of all the other parameter weighting amounts is 50%.
As the preferred version of such scheme, all the other parameters in step a also comprise the added time.
Further, at least two application that have identical optimization rank, further comprising the steps of:
The d time added according to it sorts again, added application recently, and its rank is forward.
The invention has the beneficial effects as follows: this method is simply efficient, can more objectively and impartially carry out rank to application, the rank of the rough software of containment, the healthy and orderly development in maintenance application shop.The present invention is applicable to use in the shop.
Embodiment
Below in conjunction with embodiment, describe technical scheme of the present invention in detail.
Application rank optimization method of the present invention, before improving, the single utilization download carries out the method for rank to application, proposes to utilize the multiple parameters Comprehensive Assessment to use rank.
Embodiment
Utilize mode and the feedback mode of application based on this area user commonly used, utilize download, user comment amount parameter, application activating amount parameter in the present invention, use discharging quantity parameter and added time parameter as the critical parameter of using rank, being described as follows of parameters:
Download: in expression a period of time, the user downloads the quantity of this software, needs to get rid of the repeated downloads amount.Need consider the download of many days, in order to avoid parameter value too limits to unilateral.
User comment amount: in the industry this parameter is divided into 5 grades, is selected according to experience by user self.The first estate is 5 star comments, represents that this Software Quality is high-quality, as the mark post of similar software.The second grade is 4 star comments, represents this Software Quality for better, and it is better than like product.The tertiary gradient is 3 star comments, represents that this Software Quality is common, and software class similar with major part seemingly.The fourth estate is 2 star comments, represents that this Software Quality is relatively poor, and its quality is lower than like product.The 5th grade is 1 star comment, represents that this Software Quality is extreme difference, and its product has quality to have fraud well below like product or product.The user just can comment on after must downloading this software, in order to avoid third company is maliciously commented on.Need consider the user comment amount of many days, in order to avoid parameter is unilateral, lose just.
Application activating amount: the applicable cases of this parameter reaction user to software, and the interior number of users of opening this software of a period of time.Consider in time to activate and to install after certain customers' down load application and open this applications, so consider that the activation amount of many days estimates.
Use discharging quantity: this Parametric Representation is applied in the quantity of unloading in a period of time, the dependency degree of reaction user to software.
The added time: represent that this software is submitted to the time in software application shop.
Concrete account form of the present invention is by total download, fraction to four parameter.Its weight ratio is followed successively by: download accounts for 50%, activates application quantity and accounts for 25%, and the comment amount accounts for 15%, unloading application quantity 10%.Use final ranking according to above four calculation of parameter gained, if there are a plurality of application to have identical final ranking, the time added according to it sorts again, added application recently, and its rank is forward.
Based on the Parameters Calculation method of giving tacit consent in industry, calculate the parameter of parameters:
Daily downloads * 3+ daily downloads * 2 of going forward before download parameter=today download * 8+ download * 5+ yesterday;
User comments amount parameter = (today the number of five-star reviews × 16 × 15% + today's four-star review several × 16 × 15% × 0.5 + today Samsung Comments - Today two star reviews several × 16 × 15% - today a Star Comments × 16 × 15% × 2) + (yesterday Map Comments × 16 × 15% × 80% + Prev four weeks Comments × 16 × 15% × 0.5 × 80% + Prev Samsung Comments × 80% - two weeks yesterday Comments × 16 × 15% × 80% - yesterday Star Comments × 16 × 15% × 2 × 80%) + (Prev Map Comments × 16 × 15% × 60% + Prev × 16 × four weeks Comments 15% × 0.5 × 60% + day before Samsung comments × 60% - the day before the two star reviews several × 16 × 15% × 60% - the day before a star reviews several × 16 × 15% × 2 × 60%) + (on the day before Five Star comments × 16 × 15% × 40% + on the day before the four-star review several × 16 × 15% × 0.5 × 40% + on the day before the Samsung comments × 40% - on the day before the two star reviews several × 16 × 15% × 40% - on the day before the one star reviews several × 16 × 15% × 2 × 40%);
The application activating amount parameter=today application activating quantity * 4+ application activating yesterday quantity * 2.5+ application activating day before yesterday quantity * 1.5+ upper day before yesterday of application activating quantity;
Use discharging quantity and use discharging quantity * 1.1 parameter=today;
Calculate final ranking, its formula is:
Final ranking=parameter download * 50%+ user comment amount parameter * 25%+ application activating amount parameter * 15% – uses discharging quantity parameter * 10%;
To at least two application that have identical final ranking, the time added according to it sorts again, added application recently, and its rank is forward.
Claims (6)
1. use the rank optimization method, it is characterized in that, comprise the following steps:
The a system reads at least two kinds of parameters, and wherein a kind of is download, and all the other parameters are user comment amount and/or application activating amount and/or use discharging quantity;
B calculates respectively the parameter of each parameter;
C with the parameter of above-mentioned each parameter respectively with its weighted volumes rank that is optimized after merging that multiplies each other.
2. application rank optimization method as claimed in claim 1, is characterized in that, the download in step a is for getting rid of repeated downloads amount value afterwards.
3. application rank optimization method as claimed in claim 1, is characterized in that, in step b, the Parameters Calculation method of each parameter is respectively
Daily downloads * 3+ daily downloads * 2 of going forward before download parameter=today download * 8+ download * 5+ yesterday;
User comments amount parameter = (today the number of five-star reviews × 16 × 15% + today's four-star review several × 16 × 15% × 0.5 + today Samsung Comments - Today two star reviews several × 16 × 15% - today a Star Comments × 16 × 15% × 2) + (yesterday Map Comments × 16 × 15% × 80% + Prev four weeks Comments × 16 × 15% × 0.5 × 80% + Prev Samsung Comments × 80% - two weeks yesterday Comments × 16 × 15% × 80% - yesterday Star Comments × 16 × 15% × 2 × 80%) + (Prev Map Comments × 16 × 15% × 60% + Prev × 16 × four weeks Comments 15% × 0.5 × 60% + day before Samsung comments × 60% - the day before the two star reviews several × 16 × 15% × 60% - the day before a star reviews several × 16 × 15% × 2 × 60%) + (on the day before Five Star comments × 16 × 15% × 40% + on the day before the four-star review several × 16 × 15% × 0.5 × 40% + on the day before the Samsung comments × 40% - on the day before the two star reviews several × 16 × 15% × 40% - on the day before the one star reviews several × 16 × 15% × 2 × 40%);
The application activating amount parameter=today application activating quantity * 4+ application activating yesterday quantity * 2.5+ application activating day before yesterday quantity * 1.5+ upper day before yesterday of application activating quantity;
Use discharging quantity and use discharging quantity * 1.1 parameter=today.
4. application rank optimization method as claimed in claim 1, is characterized in that, in step c, the weighted volumes of download accounts for 50%, and the summation of all the other parameter weighting amounts is 50%.
5. according to 1 to the 4 described application rank of any one claim optimization method, it is characterized in that, all the other parameters in step a also comprise the added time.
6. application rank optimization method as claimed in claim 5, is characterized in that, and is at least two application that have identical optimization rank, further comprising the steps of:
The d time added according to it sorts again, added application recently, and its rank is forward.
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Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103455938A (en) * | 2013-09-03 | 2013-12-18 | 小米科技有限责任公司 | Data-processing method and device and server equipment |
| CN103559208A (en) * | 2013-10-10 | 2014-02-05 | 北京智谷睿拓技术服务有限公司 | Application ranking fraud detection method and system |
| CN103577542A (en) * | 2013-10-10 | 2014-02-12 | 北京智谷睿拓技术服务有限公司 | Ranking fraud detection method and ranking fraud detection system of application program |
| CN103577541A (en) * | 2013-10-10 | 2014-02-12 | 北京智谷睿拓技术服务有限公司 | Ranking fraud detection method and ranking fraud detection system of application program |
| CN103761228A (en) * | 2013-10-10 | 2014-04-30 | 北京智谷睿拓技术服务有限公司 | Ranking threshold determination method and ranking threshold determination system for application program |
| CN104932966A (en) * | 2015-06-19 | 2015-09-23 | 广东欧珀移动通信有限公司 | Method and device for detecting false downloading times of application software |
| CN105630503A (en) * | 2015-12-28 | 2016-06-01 | 北京大学 | Selecting method for mobile device application and development based on operating records of user |
| CN105787287A (en) * | 2016-05-06 | 2016-07-20 | 广州爱九游信息技术有限公司 | System, equipment, device and method for generating list data |
| CN105893154A (en) * | 2016-03-31 | 2016-08-24 | 联想(北京)有限公司 | Flow distribution method and server |
| CN105912599A (en) * | 2016-03-31 | 2016-08-31 | 维沃移动通信有限公司 | Ranking method and terminal of terminal application programs |
| CN103605754B (en) * | 2013-11-22 | 2017-02-01 | 北京飞流九天科技有限公司 | Method and device for ranking applications |
| CN106874416A (en) * | 2017-01-23 | 2017-06-20 | 腾讯科技（深圳）有限公司 | Seniority among brothers and sisters list generation method and ranking list single generating device |
| CN107463578A (en) * | 2016-06-06 | 2017-12-12 | 工业和信息化部电信研究院 | Using download statistics De-weight method, device and terminal device |
| CN107911345A (en) * | 2017-10-27 | 2018-04-13 | 广东欧珀移动通信有限公司 | Subscription list of playing generation method, device and server |
| US10606845B2 (en) | 2013-10-10 | 2020-03-31 | Beijing Zhigu Rui Tuo Tech Co., Ltd | Detecting leading session of application |
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| EP2533177A1 (en) * | 2011-06-07 | 2012-12-12 | Research In Motion Limited | Application ratings based on performance metrics |
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Cited By (26)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103455938A (en) * | 2013-09-03 | 2013-12-18 | 小米科技有限责任公司 | Data-processing method and device and server equipment |
| CN103455938B (en) * | 2013-09-03 | 2016-07-20 | 小米科技有限责任公司 | A kind of data processing method, device and server apparatus |
| CN103577541A (en) * | 2013-10-10 | 2014-02-12 | 北京智谷睿拓技术服务有限公司 | Ranking fraud detection method and ranking fraud detection system of application program |
| CN103559208B (en) * | 2013-10-10 | 2017-03-01 | 北京智谷睿拓技术服务有限公司 | The ranking fraud detection method of application program and ranking fraud detection system |
| CN103761228A (en) * | 2013-10-10 | 2014-04-30 | 北京智谷睿拓技术服务有限公司 | Ranking threshold determination method and ranking threshold determination system for application program |
| WO2015051752A1 (en) * | 2013-10-10 | 2015-04-16 | Beijing Zhigu Rui Tuo Tech Co., Ltd | Ranking fraud detection for application |
| CN103577542A (en) * | 2013-10-10 | 2014-02-12 | 北京智谷睿拓技术服务有限公司 | Ranking fraud detection method and ranking fraud detection system of application program |
| US10606845B2 (en) | 2013-10-10 | 2020-03-31 | Beijing Zhigu Rui Tuo Tech Co., Ltd | Detecting leading session of application |
| CN103559208A (en) * | 2013-10-10 | 2014-02-05 | 北京智谷睿拓技术服务有限公司 | Application ranking fraud detection method and system |
| CN103577542B (en) * | 2013-10-10 | 2018-09-25 | 北京智谷睿拓技术服务有限公司 | The ranking fraud detection method and ranking fraud detection system of application program |
| CN103761228B (en) * | 2013-10-10 | 2017-10-13 | 北京智谷睿拓技术服务有限公司 | The rank threshold of application program determines that method and rank threshold determine system |
| CN103577541B (en) * | 2013-10-10 | 2017-10-10 | 北京智谷睿拓技术服务有限公司 | The ranking fraud detection method and ranking fraud detection system of application program |
| CN103605754B (en) * | 2013-11-22 | 2017-02-01 | 北京飞流九天科技有限公司 | Method and device for ranking applications |
| CN104932966A (en) * | 2015-06-19 | 2015-09-23 | 广东欧珀移动通信有限公司 | Method and device for detecting false downloading times of application software |
| CN104932966B (en) * | 2015-06-19 | 2017-09-15 | 广东欧珀移动通信有限公司 | Detect that application software downloads the method and device of brush amount |
| CN105630503B (en) * | 2015-12-28 | 2018-08-21 | 北京大学 | A kind of application and development mobile device choosing method based on user operation records |
| CN105630503A (en) * | 2015-12-28 | 2016-06-01 | 北京大学 | Selecting method for mobile device application and development based on operating records of user |
| CN105912599A (en) * | 2016-03-31 | 2016-08-31 | 维沃移动通信有限公司 | Ranking method and terminal of terminal application programs |
| CN105893154A (en) * | 2016-03-31 | 2016-08-24 | 联想(北京)有限公司 | Flow distribution method and server |
| CN105893154B (en) * | 2016-03-31 | 2019-07-26 | 联想(北京)有限公司 | A kind of flow allocation method and server |
| CN105787287A (en) * | 2016-05-06 | 2016-07-20 | 广州爱九游信息技术有限公司 | System, equipment, device and method for generating list data |
| CN107463578A (en) * | 2016-06-06 | 2017-12-12 | 工业和信息化部电信研究院 | Using download statistics De-weight method, device and terminal device |
| CN107463578B (en) * | 2016-06-06 | 2020-01-14 | 工业和信息化部电信研究院 | Application download amount statistical data deduplication method and device and terminal equipment |
| CN106874416A (en) * | 2017-01-23 | 2017-06-20 | 腾讯科技（深圳）有限公司 | Seniority among brothers and sisters list generation method and ranking list single generating device |
| CN107911345A (en) * | 2017-10-27 | 2018-04-13 | 广东欧珀移动通信有限公司 | Subscription list of playing generation method, device and server |
| CN107911345B (en) * | 2017-10-27 | 2020-07-24 | Oppo广东移动通信有限公司 | Game reservation list generation method and device and server |
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Application publication date: 20130626 |